Predicting Clinical Needs Derived from the COVID-19 Pandemic: The Case of Spain

Abstract

Background: The evolution of the pandemic caused by COVID-19, its high reproductive number and the associated clinical needs, is overwhelming national health systems. We propose a method for predicting both ICU requirements and the number of deaths, and which will enable the health authorities of the countries involved to plan the resources needed to face the pandemic as many days in advance as possible. Methods: We use official Spanish data to predict ICU admissions and deaths based on the number of infections. We employ OLS to perform the econometric estimation, and through RMSE, MSE, MAPE, and SMAPE forecast performance measures we select the best lagged predictor of both dependent variables. Findings: For Spain, our prediction shows that the best predictor of ICU admissions is the number of people infected eight days before, and that the best predictor of deaths is the number of people infected five days before. In the first case, we obtain a 98% coefficient of determination, and in the second a 97% coefficient. The estimated delayed elasticities find that a 1% increase in the number of cases today will imply a 0.72% increase in ICU patients eight days later and a 1.09% increase in the number of deaths five days later. Interpretation: The model is not intended to analyse the epidemiology of COVID-19. Our objective is rather to estimate a leading indicator of clinical needs. Having a forecast model available several days in advance can enable governments to more effectively face the gap between needs and resources triggered by the outbreak and thus reduce the deaths caused by COVID-19. Funding Statement: No funding Declaration of Interests: None.